Essential Maths Skills For Exploring Social Data

Are you feeling anxious about statistics? This hands-on workbook is packed with clear explanations, real-world examples and practical exercises to give you the maths skills you need to tackle statistics with confidence. Grounded in a social science context, it features: · Clear introductions to symbols and tasks · Multiple choice questions and exercises · Real world case studies and datasets · A glossary of terms To help you practice, this book is accompanied by useful online resources, including datasets and questions, as well as a selection of author videos.

Full of practical advice and real-world examples, this step-by-step guide offers you an accessible introduction to doing quantitative social research using Microsoft Excel.

"More than ever before, modern social scientists require a basic level of mathematical literacy, yet many students receive only limited mathematical training prior to beginning their research careers. This textbook addresses this dilemma by offering a comprehensive, unified introduction to the essential mathematics of social science. Throughout the book the presentation builds from first principles and eschews unnecessary complexity. Most importantly, the discussion is thoroughly and consistently anchored in real social science applications, with more than 80 research-based illustrations woven into the text and featured in end-of-chapter exercises. Students and researchers alike will find this first-of-its-kind volume to be an invaluable resource."--BOOK JACKET.

In a new textbook designed for students new to statistics and social data, Stephen Gorard focuses on non-inferential statistics as a basis to ensure students have basic statistical literacy. Understanding why we have to learn statistics and seeing the links between the numbers and real life is a crucial starting point. Using engaging, friendly, approachable language this book will demystify numbers from the outset, explaining exactly how they can be used as tools to understand the relationships between variables. This text assumes no previous mathematical or statistical knowledge, taking the reader through each basic technique with step-by-step advice, worked examples, and exercises. Using non-inferential techniques, students learn the foundations that underpin all statistical analysis and will learn from the ground up how to produce theoretically and empirically informed statistical results.

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

The SAGE Dictionary of Statistics provides students and researchers with an accessible and definitive resource to use when studying statistics in the social sciences, reading research reports and undertaking data analysis.

Empower students to be the change—join the teaching mathematics for social justice movement! We live in an era in which students have —through various media and their lived experiences— a more visceral experience of social, economic, and environmental injustices. However, when people think of social justice, mathematics is rarely the first thing that comes to mind. Through model lessons developed by over 30 diverse contributors, this book brings seemingly abstract high school mathematics content to life by connecting it to the issues students see and want to change in the world. Along with expert guidance from the lead authors, the lessons in this book explain how to teach mathematics for self- and community-empowerment. It walks teachers step-by-step through the process of using mathematics—across all high school content domains—as a tool to explore, understand, and respond to issues of social injustice including: environmental injustice; wealth inequality; food insecurity; and gender, LGBTQ, and racial discrimination. This book features: Content cross-referenced by mathematical concept and social issues Downloadable instructional materials for student use User-friendly and logical interior design for daily use Guidance for designing and implementing social justice lessons driven by your own students’ unique passions and challenges Timelier than ever, teaching mathematics through the lens of social justice will connect content to students’ daily lives, fortify their mathematical understanding, and expose them to issues that will make them responsive citizens and leaders in the future.

The updated edition of this classic text introduces a range of techniques for exploring quantitative data. Beginning with an emphasis on descriptive statistics and graphical approaches, it moves on in later chapters to simple strategies for examining the associations between variables using inferential statistics such as chi squared. The book has been substantially revised to include the most recent approaches to data analysis, and includes step-by-step instructions on using SPSS. All these techniques are illustrated with intriguing real examples, drawn from important social research over the past three decades, designed to illuminate significant sociological and political debates. The book shows how students can use quantitative data to answer various questions: Is it true that the rich are getting richer and the poor are getting poorer? Are crime rates really going down, and how can we tell? How much alcohol do men and women really drink in an average week? Which country in Europe has the highest average working hours? Readers are encouraged to explore data for themselves, and are carefully guided through the opportunities and pitfalls of using statistical packages, as well as the numerous data sources readily available online. Suitable for those with no previous experience of quantitative data analysis, the second edition of Exploring Data will be invaluable to students across the social sciences. Download answers to exercises in book.